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Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Research finds that vision-language models like CLIP primarily rely on class names for descriptions, not visual evidence, leading to poor zero-shot accuracy.
Open sourceOneBench interpretation
Institutional assessment
So what
This research exposes a fundamental limitation in current vision-language models' ability to generate truly visually grounded explanations, impacting their reliability for sensitive image classification tasks.
Do what
Your model validation framework for any multimodal AI applications must explicitly test for reliance on linguistic cues over actual visual evidence, especially for tasks requiring explainability or robustness to OOD data.